Valence, energy, major/minor and tempo

In terms of energy/valence the two discover weeklies are distributed fairly similar. However Weekly 1 is more scattered whereas weekly 2 can be divided into two groups (seen in next plot). Loudness is pretty much the same among all songs. Both playlists consist of mostly major songs which, honestly, was to my surprise. Something interesting in weekly 2 is that the songs with highest valence and highest energy are songs in a minor key.

Change of energy in songs in the discover weekly playlists

Keys

Weekly 1 seems to be mostly in “A”. This makes sense because the songs in that playlist have a lot more simplicity to them. And A is a pretty “go to” key in my opinion.

Chromagraphs for two versions of Paranoid android

Trying to find a dynamic warp path for these two versions is quite hard

Due to the completely different timings in the song and generally different approach to the song, they are completely different songs according to Dynamic Time Warping. Let’s take a look at another example.

“Hey Joe”, both covers, one very popular, one less popular (but really should be)

DTW path is even less observable now

Even though when you listen to both songs you can immediately link them (probably due to the lyrics), they are nothing alike due to DTW.

Still, when I listen to these two covers I feel that they are more similar than the two “Paranoid Android” versions which does contain some clearer warping paths (upper right area)

(Side note if you don’t know the Roy Buchanan version and you like the Jimi Hendrix version really give Roy’s version a listen)

Twelve bar blues

DTW

I’m pretty sure there is a example that shows two 12 bar blues tracks with a really visible dtw line. But I haven’t found the example yet.

Concluding thoughts

Covers don’t have to sound like eachother. (Will expand in the future)

I Am pretty sure I will be removing this because I do not think this will be interesting for my portfolio. However analysis of chroma might be useful for similarity measures (Turning every DTW into a number by summing all the distances or something to see how similar two songs are chroma wise (very costly though)).

Miss Alissa by Eagles of Death metal and Heather by Billy Cobham

So I took a look in terms of valence and energy for two songs that should be polar opposites according to valence and energy and boy is that right.

In the lower left corner we have, with a valence of 0.041, an energy rating of 0.0079 and a length of 8 minutes and 39 seconds, the smooth and shockingly calming “Heather” by Billy Cobham.

In the opposing upper right corner we have, with a valence of 0.877, energy rating of 0.992 and a length of 2 minutes and 38 seconds, you can’t stop dancing to this one, “Miss Alissa” by Eagles of Death Metal.

I analysed differences in chromagrams, cepstograms and self similarity matrices.

Chroma comparisson

Miss Alissa seems to be all over the place in terms of chroma, whilst Heather is more organized and stays close to F for the whole duration of the song. Of course Heather is a lot longer so it leaves a lot more room for organization and build up.

MFCC comparisson

Miss Alissa seems to mostly concentrated in the first cepstral coefficients whilst Heather is concentrated more in the second and third.

First cepstral coefficient referring to loudness seems appropriate because Miss Alissa is quite loud and Heather is quite quiet.

Around 220 seconds the saxophone joins the song Heather which is observable in the cepstogram due to the distribution of magnitude getting more spread out.

Self similarity matrices Timbre

The self similarity matrices in terms of timbre show that Miss Alissa is fairly constant in terms of timbre whilst Heather shows a big change around 220 seconds which is because of the saxophone taking the lead.

Self Similarity matrices Pitch

Now it’s funny to see that the chromagram of Miss Alissa is complete chaos but the self similarity matrix is fairly constant and looks like one block implying that it might be quite chaotic but the chaos is very constant, which is actually a pretty good dexcription for the song.

On the other hand there is Heather that has a very organized chromagram that looks fairly constant but the self similarity matrix in terms of pitches looks a lot more complex than that of Miss Alissa.

Unfortunate Message

Unfortunately my laptop kept crashing when trying to make tempograms. I am going to look into this because I would like to include them into my portfolio.

Clustering

I thought it might be fun to see if I could cluster a day of songs into 6 clusters which resemble daily mixes. For this reason hierarchical clustering did not seem like the best option for it will be hard to pick 6 clusters with hierarchical clustering because it always branches out in 2. Meaning that I’d have to pick a depth with either 4 clusters or 8 clusters.

Therefore I have used k-means with k=6 to find 6 clusters.

I used energy, key, loudness, mode, speechiness, acousticness, instrumentalness, liveness, valence, tempo, time_signature, track.duration_ms and the MFCC’s as features for clustering.

Here you can see a graph of the energy and valence of the daily mixes (top) and the clusters (bottom). It is fairly apparent that valence and energy do not have a lot of say in distinguishing both daily mixes and k-means clusters.

Let’s find the important features.

Training a classifier to determine cluster important features

# A tibble: 6 x 3
  class precision recall
  <fct>     <dbl>  <dbl>
1 1         0.934  1    
2 2         0.926  0.893
3 3         0.970  0.914
4 4         0.933  0.848
5 5         0.929  0.743
6 6         0.898  0.981

Important features for clusters